Lecture notes

  1. Descriptive statistics and graphics (PDF)
  2. Parameter estimation, sampling and experiment design (PDF)
  3. Statistical models and confidence intervals (PDF)
  4. Hypothesis testing (PDF)
  5. Chi-squared test and ANOVA (PDF)
  6. Simple linear regression (PDF) - Data
  7. Multiple linear regression (PDF) - Data 1 2 3
  8. Model evaluation and selection (PDF) - Data
  9. Logistic regression (PDF)
  10. Poisson regression (PDF) - Data 1 2
  11. Mixed models - Part 1 (PDF) - Data
  12. Mixed models - Part 2 (PDF) - Data
  13. Multivariate analysis - Part 1 (PDF) - Data
  14. Multivariate analysis - Part 2 (PDF)
  15. Course synthesis (PDF)

Labs

  1. Introduction to R - Part 1 (PDF) - Data
  2. Introduction to R - Part 2 (PDF) - Worksheet - Data 1 2
  3. Sampling and parameter estimation (PDF)
  4. Hypothesis testing (PDF) - Data 1 2
  5. Chi-squared test and ANOVA (PDF)
  6. Simple linear regression (PDF) - Data
  7. Multiple linear regression (PDF) - Data 1 2
  8. Model selection (PDF) - Data 1 2(a) 2(c)
  9. Logistic regression (PDF)
  10. Poisson regression (PDF) - Data 1 2
  11. Mixed models (PDF)
  12. Multivariate analysis (PDF) - Data 1 2